AI in HAI Prevention: A Sustainable Approach

The integration of artificial intelligence (AI) marks a transformative shift and nowhere is this shift more critical than in infection prevention within hospital settings.

This article originally appeared on our sister site, Infection Control Today.

In health care, where every decision impacts patient well-being and operational efficiency, the integration of artificial intelligence (AI) marks a transformative shift. Nowhere is this shift more critical than in infection prevention within hospital settings. The toll of hospital-associated infections (HAIs) on both patient health and health care budgets is staggering, adding billions to treatment costs annually in the US alone.

Ravinder Singh, senior vice president of consulting at CitiusTech, explained to Infection Control Today how AI is reshaping infection prevention in hospitals, driving efficiencies, and safeguarding both patients and the community.

ICT: Can you explain AI revolutionizes infection prevention in hospital settings?

Ravinder Singh: The deeper we delve into artificial intelligence, the more evident it becomes that its applications span multiple aspects of health care, driving the triple aim of quality of care at the lowest cost and with the best experience for patients. Hospital-associated infections (HAI) add anywhere between $25 billion and $45 billion to the cost of treatment in the US health care system. Leveraging technology and AI to create the right surveillance, controls, and prevention can greatly impact delivering better patient care.

Hospitals are now maturing to connected and interoperable systems, devices, and labs, providing high-quality and timely data that offers a foundation for real-time surveillance of hospital infections, identification of most effective preventive measures, and, finally, enabling adherence to infection control protocols.

Leveraging AI enables advancing standard and generic infection control methods to a risk-based model and create a targeted control plan. For example, understanding patterns of post-operative infections vs. central line infections or ventilator-associated pneumonia can help drive prevention plans at the procedure level or even be personalized to a patient’s treatment journey.

AI models can also further build simulations illustrating the dire consequences of ‘inaction’ in infection control. By advocating for proactive measures, these models play a pivotal role in curbing hospital-acquired infections, be they bacterial, viral, or fungal. They can be used to curb the rampant overuse of antibiotics, thereby mitigating the emergence of resistant strains.

ICT: What AI technologies or tools detect and prevent infections within health care facilities?

RS: The integration of predictive analytics allows for risk stratification, particularly in individual or patient-level infection management. An AI-powered comprehensive patient data analysis—from medical history and current condition to planned procedures and past antibiotic usage—can unravel several actionable insights. Treatment based on an individual’s susceptibility to specific infections, the most suitable antimicrobial therapy for targeted treatment, enhances patient outcomes.

The simulation of HAI outbreaks represents a groundbreaking application of AI in surveillance and prevention efforts. By leveraging available data, AI algorithms can generate sophisticated simulations of potential outbreaks, such as those caused by methicillin-resistant Staphylococcus aureus (MRSA) or Clostridium difficile. AI helps us understand why infections happen and how they spread. This insight allows us to act early to stop outbreaks, keeping patients safe

Adopting AI-enhanced surveillance and monitoring systems represents a paradigm shift in healthcare, endorsed even by organizations like the World Health Organization (WHO). AI algorithms can scrutinize electronic medical records (EMRs), prescription patterns, laboratory and radiological results, and even hospital CCTV footage to flag lapses in hygiene protocols, such as inadequate handwashing or improper use of personal protective equipment (PPE). Moreover, AI models minimize human error or bias, elevating system standards.

ICT: How does AI enhance the early detection of potential outbreaks or clusters of infections in hospitals, and what are the benefits of this rapid identification?

RS: With the wealth of data available, AI can analyze various scenarios and run simulations to predict potential sources of outbreaks. This helps in taking proactive measures to prevent such occurrences.

Infections can vary not only between patients but also based on their location within the hospital, such as a ward, an emergency room bed, or an intensive care unit. Factors like the composition of the care team, room sharing, the health status of other patients, ongoing procedures, and pre-existing dormant infections further contribute to the complexity.

Predicting and preventing infections brings several benefits, including cost savings in health care, shorter hospital stays, better clinical outcomes, and enhanced experiences for patients and providers. Preventing major outbreaks contributes to safeguarding hospitals’ reputations.

ICT: How can AI-driven predictive analytics help health care professionals make informed decisions to prevent the spread of infections?

RS: It’s important to recognize that AI is a tool meant to support health care professionals, not replace them. Its primary aim is to enhance efficiency and avoid burnout among medical staff.

AI models can significantly improve clinical decision-making by providing timely support to physicians. For instance, they can suggest the most appropriate antibiotic based on a patient’s history, even identifying options that physicians might not have considered. This guidance helps optimize treatment plans and improves patient outcomes.

In conditions like sepsis, AI models can help select the initial antibiotic treatment. Rather than resorting to the strongest available option, AI can recommend a more targeted approach based on the patient’s specific condition. This proactive strategy can lead to more effective treatment while awaiting culture and sensitivity reports (which typically take up to 48 hours).

ICT: Could you share examples of successful implementations of AI in infection prevention programs and how these technologies have improved patient outcomes and reduced HAIs?

RS: At BID Medical Center in Boston, USA, an AI system has revolutionized the early detection of sepsis caused by organisms like Escherichia coli and Staphylococcus. This AI system outperforms traditional methods by providing more accurate and faster detection. By pinpointing patients at risk of developing sepsis, the AI system alerts physicians to intervene promptly, potentially preventing a life-threatening condition from worsening.

In Europe, hospitals such as the Amsterdam Medical Center in the Netherlands are piloting AI algorithms to predict the risk of patients developing healthcare-associated infections (HAIs). This innovative approach has prompted the implementation of stricter protocols, including improved hand hygiene, double-gloving, and PPE during procedures on high-risk patients. Additionally, there’s a heightened focus on more rigorous cleaning of operating rooms after treating such patients. These measures significantly improve patient safety.

AI is here to stay and grow. The health care system must tap AI’s remarkable ability to help us take significant strides forward to achieve the quintuple aim of healthcare delivery: better patient outcomes, better patient experience, reduction of physician burnout, reduction of costs, and achieving health equity.

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